{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "import pandas.io.data as web\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'yahoo', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Adj Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "\n",
    "def sort_data(rets):\n",
    "    ins = []\n",
    "    outs = []\n",
    "    for i in range(len(rets)-100):\n",
    "        ins.append(rets[i:i+100].tolist())\n",
    "        outs.append(rets[i+100])\n",
    "    return np.array(ins), np.array(outs)\n",
    "        \n",
    "symbol_list = ['C', 'GS']\n",
    "rets = get_data(symbol_list)\n",
    "ins, outs = sort_data(rets)\n",
    "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n",
    "div = int(.8 * ins.shape[0])\n",
    "train_ins, train_outs = ins[:div], outs[:div]\n",
    "test_ins, test_outs = ins[div:], outs[div:]\n",
    "\n",
    "#normalize inputs\n",
    "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self, config, training=True):\n",
    "        #CONFIG\n",
    "        symbol_list = self.symbol_list = config.symbol_list\n",
    "        num_samples = self.num_samples =  config.num_samples\n",
    "        input_len = self.input_len =  config.input_len\n",
    "        n_hidden_1 = self.n_hidden_1 =  config.n_hidden_1\n",
    "        n_hidden_2 = self.n_hidden_2 =  config.n_hidden_2 \n",
    "        learning_rate = self.learning_rate = config.learning_rate\n",
    "        \n",
    "        #bucket info\n",
    "        positions = self.positions = tf.constant([-1,0,1])\n",
    "        num_positions = self.num_positions =  3\n",
    "        \n",
    "        #more vars\n",
    "        num_symbols = self.num_symbols =  len(symbol_list)\n",
    "        n_input = self.n_input = num_symbols * input_len\n",
    "        n_classes = self.n_classes = num_positions * num_symbols \n",
    "\n",
    "        \n",
    "        \n",
    "        x =self.x = tf.placeholder(tf.float32, [None, n_input])\n",
    "        y_ =self.y_= tf.placeholder(tf.float32, [None,  num_symbols])\n",
    "\n",
    "        weights = {\n",
    "            'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "            'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "            'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "        }\n",
    "        biases = {\n",
    "            'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "            'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "            'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "        }\n",
    "\n",
    "        \n",
    "        def multilayer_perceptron(x, weights, biases, keep_prob):\n",
    "            # Hidden layer with RELU activation\n",
    "            layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "            layer_1 = tf.nn.relu(layer_1)\n",
    "            # Hidden layer with RELU activation\n",
    "            layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "            layer_2 = tf.nn.relu(layer_2)\n",
    "            # Output layer with linear activation\n",
    "            out_layer_f = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "            out_layer = tf.nn.dropout(out_layer_f, keep_prob)                 # DROPOUT LAYER\n",
    "            return out_layer\n",
    "        \n",
    "        if training == True: keep_prob = 0.5  # DROPOUT\n",
    "        else: keep_prob = 1.0                 # NO DROPOUT\n",
    "            \n",
    "        # Construct model\n",
    "        y = multilayer_perceptron(x, weights, biases, keep_prob)\n",
    "\n",
    "\n",
    "\n",
    "        # loop through symbol, taking the columns for each symbol's bucket together\n",
    "        pos = {}\n",
    "        sample_n = {}\n",
    "        sample_mask = {}\n",
    "        symbol_returns = {}\n",
    "        relevant_target_column = {}\n",
    "        for i in range(num_symbols):\n",
    "            # isolate the buckets relevant to the symbol and get a softmax as well\n",
    "            symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n",
    "            symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n",
    "            # sample probability to chose our policy's action\n",
    "            sample = tf.multinomial(tf.log(symbol_probs_softmax), num_samples)\n",
    "            for sample_iter in range(num_samples):\n",
    "                sample_n[i*num_samples + sample_iter] = sample[:,sample_iter]\n",
    "                pos[i*num_samples + sample_iter] = tf.reshape(sample_n[i*num_samples + sample_iter], [-1]) - 1\n",
    "                symbol_returns[i*num_samples + sample_iter] = tf.mul(\n",
    "                                                                    tf.cast(pos[i*num_samples + sample_iter], float32), \n",
    "                                                                     y_[:,i])\n",
    "\n",
    "                sample_mask[i*num_samples + sample_iter] = tf.cast(tf.reshape(tf.one_hot(sample_n[i*num_samples + sample_iter], 3), [-1,3]), float32)\n",
    "                relevant_target_column[i*num_samples + sample_iter] = tf.reduce_sum(\n",
    "                                                            symbol_probs_softmax * sample_mask[i*num_samples + sample_iter],1)\n",
    "        self.pos = pos\n",
    "\n",
    "        # PERFORMANCE METRICS\n",
    "        daily_returns_by_symbol_ = tf.concat(1, [tf.reshape(t, [-1,1]) for t in symbol_returns.values()])\n",
    "        daily_returns_by_symbol = tf.transpose(tf.reshape(daily_returns_by_symbol_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "        self.daily_returns = daily_returns = tf.reduce_mean(daily_returns_by_symbol, 2) # [?,5]\n",
    "        \n",
    "        total_return = tf.reduce_prod(daily_returns+1, 0)\n",
    "        z = tf.ones_like(total_return) * -1\n",
    "        self.total_return =total_return= tf.add(total_return, z)\n",
    "        \n",
    "        self.ann_vol = ann_vol = tf.mul(\n",
    "            tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns, 0)),2),0)) ,\n",
    "            np.sqrt(252)\n",
    "            )\n",
    "        self.sharpe = sharpe = tf.div(total_return, ann_vol)\n",
    "        #Maybe metric slicing later\n",
    "        #segment_ids = tf.ones_like(daily_returns[:,0])\n",
    "        #partial_prod = tf.segment_prod(daily_returns+1, segment_ids)\n",
    "\n",
    "\n",
    "        training_target_cols = tf.concat(1, [tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n",
    "        ones = tf.ones_like(training_target_cols)\n",
    "        gradient_ = tf.nn.sigmoid_cross_entropy_with_logits(training_target_cols, ones)\n",
    "        gradient = tf.transpose(tf.reshape(gradient_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "\n",
    "        #L2  = tf.contrib.layers.l2_regularizer(0.1)\n",
    "        #t_vars = tf.trainable_variables()\n",
    "        #reg = tf.contrib.layers.apply_regularization(L2, tf.GraphKeys.WEIGHTS)\n",
    "\n",
    "        #cost = tf.mul(gradient , daily_returns_by_symbol_reshaped)\n",
    "        #cost = tf.mul(gradient , tf.expand_dims(daily_returns, -1)) #+ reg\n",
    "        #cost = tf.mul(gradient , tf.expand_dims(total_return, -1))\n",
    "        cost = tf.mul(gradient , tf.expand_dims(sharpe, -1))\n",
    "\n",
    "        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "        self.costfn = tf.reduce_mean(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SmallConfig(object):\n",
    "    \"\"\"Small config.\"\"\"\n",
    "    symbol_list = ['C', 'GS']\n",
    "    num_samples = 20\n",
    "    input_len = 100\n",
    "    n_hidden_1 = 50 \n",
    "    n_hidden_2 = 50 \n",
    "    learning_rate = 0.00005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "# initialize variables to random values\n",
    "with tf.variable_scope(\"model\", reuse=None):\n",
    "    m = Model(config = SmallConfig)\n",
    "with tf.variable_scope(\"model\", reuse=True):\n",
    "    mvalid = Model(config = SmallConfig, training=False)\n",
    "sess.run(tf.initialize_all_variables())\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "# run optimizer on entire training data set many times\n",
    "train_size = train_ins.shape[0]\n",
    "for epoch in range(20000):\n",
    "    start = rng.randint(train_size-50)\n",
    "    batch_size = rng.randint(2,75)\n",
    "    end = min(train_size, start+batch_size)\n",
    "    \n",
    "    sess.run(m.optimizer, feed_dict={m.x: train_ins[start:end], m.y_: train_outs[start:end]})#.reshape(1,-1).T})\n",
    "    # every 1000 iterations record progress\n",
    "    if (epoch+1)%1000== 0:\n",
    "        t,s, c = sess.run([ mvalid.total_return, mvalid.ann_vol, mvalid.costfn], feed_dict={mvalid.x: train_ins, mvalid.y_: train_outs})#.reshape(1,-1).T})\n",
    "        t = np.mean(t)\n",
    "        t = (1+t)**(1/6) -1\n",
    "        s = np.mean(s)\n",
    "        s = t/s\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\",c, \"total return=\", \"{:.9f}\".format(t), \n",
    "             \"sharpe=\", \"{:.9f}\".format(s))\n",
    "        #print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# in sample results\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "d, t = sess.run([mvalid.daily_returns, mvalid.pos[0]], feed_dict={mvalid.x: train_ins, mvalid.y_: train_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MjA4/b7uSvRDCAcwA3pNSftFKke1A30bP08Lbmmic7DuLz+ejW6I1VYKjEtyu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\ncNONVs8cyRRefDGZc86xumH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mQVTNR9QRz4QJEB8/F8NwU6fD/96xat8PPng/N9xwA5WV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\nNDQ0LgL+H/LKAy4Bwr41AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe8ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equity curve\n",
    "for i in range(mvalid.num_samples):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12ade5ac8>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BHi1tRluHiQbNpnPclUdL20ITyBEra0x8Nmt7vygz6FY9be7jNn7XRB0Myp5ATlNej5bW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\ngoGIiKBgICIiwP8DZxbBmMpvefYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe8f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#out of sample results\n",
    "d, t = sess.run([mvalid.daily_returns, mvalid.pos[0]], feed_dict={mvalid.x: test_ins, mvalid.y_: test_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O0mZhLj9I8PhghF5gSu2/+JtSTDTlN3U5Hms085dTH+ZAa+hnlt3O1vp6ZgUH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7YhnmgRzPMfC5QaY7YZYxqAhYB/9e5QYBmAMeYzIFdEuoe4r8VhTEoKXzU0UObXfVTus2ZwisvF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\n/C0Wi6UL8v+d9NEd0E6leQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1307a0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out of sample results\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1306c88d0>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QymCNgQIarAFwCqj6dAqomVyxZ3NvVnO6dQpIy23VIesByigg6eVa5TGkZzpk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\nDsdI4QbA4XA4Ror/D446Kf4q/femAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12adc2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
